Hard c-means can be used for building classifiers in supervised machine learning. For example, in a n-class problem, c clusters are built for each of the classes. This results into n · c centroids. Then, new examples can be classified according to the nearest centroid. In this work we consider the problem of building classifiers using fuzzy clustering techniques. In particular, we consider the use of fuzzy c-means, as well as some variations. Namely, fuzzy c-means with variable size and entropy based fuzzy c-means.